Stochastic thresholds in hemostasis and thrombosis: A multiscale biophysical framework from transcriptional bursting...

Hemostasis and thrombosis involve tightly regulated biological processes that operate near critical activation thresholds across molecular, cellular, mechanical, and biochemical scales. Traditional descriptions of coagulation emphasize deterministic cascade models; however, increasing evidence indicates that variability and stochastic dynamics substantially influence activation probability. This review outlines a conceptual multiscale biophysical framework that connects published experimental and computational findings on transcriptional bursting, platelet mechanotransduction, force-dependent receptor stabilization, and spatiotemporal thrombin propagation. At the molecular level, stochastic promoter switching generates fluctuations in protein abundance, shaping the probability of exceeding functional concentration thresholds. At the cellular scale, nanoscale surface architecture and receptor density modulate the time required for integrin clustering under shear flow, thereby altering adhesion stability. At the receptor-ligand interface, tensile force reshapes energy landscapes, producing catch-bond behavior that enhances bond lifetime within defined force regimes as demonstrated in prior molecular dynamics simulations. At the biochemical level, thrombin generation and fibrin polymerization exhibit nonlinear reaction-diffusion dynamics, where activation depends on crossing a critical concentration threshold. These processes can be unified via first-passage concepts, in which pathological activation corresponds to probabilistic boundary crossing rather than simple elevation of mean biomarker levels. This perspective shifts emphasis toward distributional properties, temporal variability, and amplification kinetics. By integrating stochastic modeling with published experimental data, this framework provides a mechanistic basis for understanding interindividual variability in thrombotic risk and suggests new avenues for quantitative risk assessment and therapeutic intervention. We emphasize that the framework presented here is conceptual and integrative rather than fully parameterized; we identify the quantitative parameters and cross-scale measurements that would be required to construct a fully predictive model in future work.